AIPOCH Open Science
Open-source, local-first, model-agnostic AI research workbench for reproducible science.
Open Science is an open-source, local-first, model-agnostic AI research workbench developed by AIPOCH for
scientists and researchers. It enables reproducible, inspectable research with scientific AI agents, Python and R execution, scientific data
connectors, and cross-platform support for macOS, Windows, and Linux. Create a project, describe your research goal in plain language, and let the
agents read files, search the web, run code, query scientific data sources, and produce reports, tables, and figures with traceable provenance—all in
one workspace.
Open Science supports computational and data-intensive research across disciplines, including machine learning, statistics, life sciences, chemistry,
materials science, physics and environmental science. It supports the research process from literature review and hypothesis development to code
execution, data analysis, simulation, visualization, and the production of traceable research outputs.
💡 Open Science v0.26.0 released (last updated September 2026). Open Science v0.26.0
brings HPC-class compute and a literature workspace: remote compute hosts gain a per-host Slurm execution mode alongside direct SSH, and a new
reference library organizes references, PDFs, and citations with identifier-aware imports, duplicate merging, open-access full-text attachment, and
citation formatting. Apodex joins the built-in providers alongside the latest OpenAI and Anthropic models, notebook tool calls become readable summary
cards, and smoother streaming, quieter default permissions, and a broad set of fixes land throughout. See the [latest release
notes](https://github.com/aipoch/open-science/releases/latest) for full details.
Table of Contents
- Quick Start - Product Tour - Benchmark Performance - [Why Open
Science](#why-open-science) - Design Principles - Core Capabilities - Model Providers
-
Data, Permissions, and Trust - Project Status - Development & Packaging
-
Roadmap - Relationship to the AIPOCH Ecosystem - What This Is Not -
Frequently Asked Questions - Get Involved - License - Star History
🚀 Quick Start
Get Open Science running in three steps: download the installer for your platform, complete the guided first-run setup, and create a research project.
1. Download the app
Open the latest release, expand Assets, and choose the installer for your computer:
| Your computer | Choose | | ----------------------------------- | ---------------------------------------- | | macOS — Apple Silicon (M1 or newer) |
The macOS DMG for Apple Silicon / ARM64 | | macOS — Intel | The macOS DMG for Intel / x64 | | Windows x64 | The Windows x64 installer | | Linux x64 |
The Linux x64 AppImage or Debian package |
Review the assets and verification information published on the release page. See Verifying your download before installation if you need to validate a package.
If macOS or Windows shows an unidentified-developer or unknown-publisher warning, verify that the package came from the official Releases page before continuing.
2. Complete first-time setup
The first launch has five guided steps:
- Environment checks compatibility, app storage, secure credential storage, and network access. 2. Data location chooses where large
artifacts, notebooks, uploads, and environments are stored. 3. Agent runtime selects and prepares Claude Code, OpenCode, Codex, or CodeBuddy.
App-managed runtimes can be installed without requiring Node.js, npm, or an administrator password. 4. Model provider connects and tests the model
you want to use. Choose a built-in provider, a custom gateway, or an existing Claude or Codex subscription login. 5. Notebook runtime optionally
prepares app-managed Python and R environments or enables detected and manually registered interpreters for either language.
 |  |
| Host compatibility, storage, and network checks | Provider, API Key, endpoint, and model validation |
Notebook execution is optional. Every required environment and agent-runtime check must pass before Continue becomes available, and the model
connection must pass before setup finishes. Notebook and data-location settings can keep their defaults and be changed later in Settings. While a
kernel is running, a Variables view can inspect the live Python or R namespace — names, types, shapes, and previews — read-only, refreshed after each
execution.
3. Start a research project
- Click New project and give the project a stable research name and optional description. 2. Open a session and describe the goal, input data,
constraints, desired outputs, and how the result should be checked. 3. Attach source files, select a verified model, and choose an approval mode. 4.
Send the task. Inspect the agent's tool activity, approve sensitive actions, and open generated artifacts in the preview panel. 5. To explore a
different direction, edit an earlier user message and resend it on a new branch; use the message revision controls to return to either path. 6. Open
an artifact's Provenance view to inspect its versions and the available evidence behind the selected result. 7. Continue the work in later
sessions. Use @ to reference an existing project file and / to explicitly select an enabled skill.
Screenshots in this README illustrate the workflow. Labels, catalogs, and other interface details may differ from the version you install.
Product Tour
Open Science organizes research into projects and sessions so that every result can stay connected to the evidence that produced it. The sections
below walk through the workspace, artifact provenance, previews, scientific skills, and data connectors.
One workspace from task to traceable artifacts
Projects keep related sessions, uploads, generated files, and preview state together. The conversation records the agent's answer and the commands,
file reads, edits, searches, and connector calls that produced it. Each generated artifact is stored as an immutable, checksummed version. Its
Provenance view exposes the evidence Open Science could verify at creation time: producer code and execution history, referenced inputs, an
observed environment inventory, the producing conversation branch, and any version-scoped reviewer findings. Missing evidence is shown as unavailable
instead of being guessed.
 |  |
| Uploads and generated files organized by project and session | Native previews keep data and the research history side by side |
Generated reports, figures, and tables remain attached to the session and are also collected in the project file library. Preview tabs keep the active
result visible as the panel changes size, and long names preserve their identifying suffix and extension. Open Science previews common scientific
data, PDFs, Office documents (DOCX, XLSX, PPTX), images (with zoom and pan), source code with syntax highlighting, molecular structures and reactions,
and Notebook history. Preview limits do not truncate the underlying file—the full artifact stays available to the agent and external tools. Use
Cmd/Ctrl+F to search transcripts, Notebook output, and rendered pages across the workspace, or Cmd/Ctrl+K to open the project-scoped command
palette. A dark mode rounds out the workspace: toggle the theme in Settings → General and the whole shell, transcript, and renderer palette switch
without a flash. The interface is also available in German, Spanish, Chinese (Simplified and Traditional), Japanese, Korean, French, and Russian with
a runtime language switcher in Settings.
Branch a conversation without losing the original
Edit a completed user message to resend a revised prompt from that point. Open Science creates a new message branch instead of deleting the turns that
followed, and revision controls let you move between the original and alternative paths. Branch selection, tool activity, attachments, and generated
artifacts persist across project switches and restarts. Provenance remains tied to the exact branch that produced each artifact version, so exploring
a different hypothesis does not blur the record of the earlier result.
Scientific skills and data connectors
Open Science includes a growing catalog of 22 featured, file-based research skills: AlphaFold2, Boltz, Borzoi, Chai-1, Customize, DiffDock,
Environment & Packages, ESM-2, ESMFold2, Evo 2, Figure Composer, Figure Style, Indication Dossier, LigandMPNN, Literature Review, OpenFold3, Paper
Narrative, ProteinMPNN, scGPT, scvi-tools, SolubleMPNN, and Remote Compute (SSH) for submitting and harvesting long-running jobs on remote HPC
clusters. You can create personal skills, upload SKILL.md/ZIP/.skill packages, preview and import compatible skills from GitHub with optional
authenticated access, or import skills already installed in your global agent directories. The agent can also request a package import from a session
attachment or a public GitHub URL, with an app-owned preview and confirmation step before anything is written. Enabled skills can be selected directly
in the composer with /.
It also includes 24 built-in research connectors: Literature Graph, PubMed, bioRxiv, Genes & Ontologies, Genomes, BioMart, Variants, Human
Genetics, Clinical Genomics, Structures & Interactions, Protein Annotation, Expression, Omics Archives, CellGuide, Regulation, RNA, Chemistry, ChEMBL,
ZINC, Molecule Viewer, Clinical Trials, Drug Regulatory, Cancer Models, and Research Resources. Built-in and custom connectors remain behind the
permission system, with per-tool Always allow, Ask each time, and Block controls. The installed app shows the current skill, connector, and tool
catalogs.
 |  |
| Readable, reusable research skills | Scientific databases exposed as permissioned agent tools |
Benchmark Performance
🏆 #1 on BiomniBench-DA Public 50
Open Science achieved the highest ranking score in the compiled BiomniBench-DA Public 50 comparison, earning 79.05 with gpt-5.6-sol (xhigh).
The result combines a Gemini 3.1 Pro judge score of 81.04 and a DeepSeek v4-pro judge score of 77.06 through an equal-weight mean, placing
Open Science #1 among the collected Public 50 results. Explore the [BiomniBench-DA
dataset](https://huggingface.co/datasets/phylobio/BiomniBench-DA).
Why Open Science
Open Science brings research tasks, execution, files, and evidence into one local, inspectable desktop workspace.
Research work is usually split across chat windows, notebooks, local scripts, scientific databases, file browsers, and reporting tools. Context is
lost at every handoff, and the answer is often separated from the code and files that produced it.
Open Science brings those pieces into one inspectable desktop workspace:
- Work that persists. Projects, sessions, drafts, files, previews, and run history survive application restarts. - **Execution, not just
suggestions.** The agent can run commands, Python, and R, edit files, search, call connectors, and generate artifacts with the user's approval. -
Alternative paths without lost work. Revise an earlier prompt on a new message branch and switch between the resulting research directions. -
Traceable results. Immutable artifact versions retain the production evidence Open Science can verify, and explicitly mark evidence it cannot. -
Multiple model choices. Use a built-in cloud provider, a compatible custom gateway, or a Claude or Codex subscription; choose each Session's model
and reasoning effort together in the composer. - Local-first ownership. The application and project state run on your computer; external calls
happen through services you explicitly configure or approve. - Inspectability. The source code, skills, connector definitions, tool activity,
generated files, and artifact provenance are available for review. - Extensibility. Add skills and MCP connectors instead of waiting for a closed
plugin roadmap. - No seat license. Open Science is Apache-2.0 software. You pay only for the model or infrastructure you choose to use.
Open Science is an independent product built from scratch. It is not a proxy, unofficial client, or reskin of another AI research application.
Design Principles
Open Science is built on seven design principles that govern how code, data, models, and human oversight fit together: open by default, explicit
multi-provider compatibility, local-first data ownership, human-in-the-loop oversight, durable research records, composable capabilities, and honest
scientific boundaries.
- Open by default. Source code, formats, connectors, and skills should remain inspectable and forkable. - **Multi-provider with explicit
compatibility.** The app validates provider configuration and makes endpoint requirements visible instead of treating every API protocol as
interchangeable. - Local-first and data-aware. Keep project state local, surface external data flows, and make autonomy opt-in. -
Human-in-the-loop. File edits, commands, network access, and connector calls are governed by explicit approval profiles. - **Durable research
records.** Sessions, tool activity, Notebook history, and immutable artifact versions should remain reviewable after the run ends, with unavailable
evidence stated plainly. - Composable capabilities. Skills, connectors, models, previews, and future compute backends should be replaceable parts
rather than one black box. - Honest scientific boundaries. Generated output does not replace expert judgment, statistical review, or validation
against primary evidence.
Core Capabilities
Open Science combines project management, multi-model agent execution, Python and R notebooks, scientific data connectors, immutable artifact versions
with provenance, and permissioned human-in-the-loop control in one local workspace. The installed app and [latest release
notes](https://github.com/aipoch/open-science/releases/latest) are the source of truth for changing catalogs, packaging details, and newly added
options.
| Area | Core capability | | ---------------------------- |
| | Projects and sessions | Create, rename, and delete projects; maintain multiple sessions with pinning; edit completed prompts into persistent,
selectable message branches without deleting the original downstream path; persistent side conversations within a session; generated and editable
session details (title and description); restore recent work, drafts, conversation history, and preview state. | | Agent workflow |
Natural-language tasks, streamed responses, typed tool-activity cards grouped under declared purpose titles, compact summary cards for artifact writes
and notebook controls in messages and approvals, a live context-usage indicator with category-level estimates, on-demand context compaction, and
persistence across restarts, stop controls, approval pauses, a confirmation step (with a remembered preference) before closing or quitting during a
running task, a composer message queue for staging follow-up messages during a running turn, unified draft undo and redo history, branching into a new
session from completed agent messages, desktop notifications with attention reasons plus durable unread conversation badges and native attention on
blocking approvals, a cross-surface notification message center with kind- and state-coded icons, durable read state, and retained deleted targets,
structured agent clarification cards for multi-question requests with per-question answer review, text, image, and PDF annotations — selected text or
regions with click-to-reveal in the source document — that send selected context into the conversation, timeline markers for agent configuration
changes between turns, and expanded skill-load rows that show the loaded skill document, a session reading context that links up to three PDFs so the
agent can read the current page, page through the full document, and search across them, persistent agent memory that recalls project-scoped knowledge
across sessions, in-app sandboxed previews for source links in agent responses, live session status on the Home dashboard, message timing metadata
with elapsed-time and usage popovers, per-turn token usage with per-model-call usage details and a per-call context-window chart, completed-turn agent
framework and model identification, a project-scoped command palette, project-scoped frame reads, project actions and agent context, a project quick
switcher in the workspace project menu that lists other active projects with title and description previews and fuzzy search once the list grows,
refined session sidebar rows with hover previews of session title and description, session references (#) to other sessions in the composer with
turn-scoped read access, side-chat advisories injected into a running main turn, session-number lookup in global search, review-gated session plans
with durable execution contracts and CLI plan show, approve, and reject commands, smooth live response rendering, collapsible side panels, a
new-conversation keyboard shortcut, and recovery of sessions interrupted by an application restart. | | Subagent delegation | Production subagent
delegation with durable messaging and recovery, and a per-session delegation switch in the composer's agent controls that decides whether the agent
may delegate work.